MA’LUMOTLARNI NORMALIZATSIYA VA STANDARLASHTIRISH USULLARI. O‘LCHAMLARNI QISQARTIRISH VA PCA (PRINCIPAL COMPONENT ANALYSIS) AMALIYOTI

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PDF (Русский)
Nashr: Jild 3 Nomeri 57 : Наука и инновация
Bo'lim: Maqolalar
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Farg‘ona Davlat Texnika Universiteti
uktamovmahmudjon04@gmail.com

Abstrak:

Maqolada ma'lumotlarni normalizatsiya va standarlashtirish usullari va ularning mashinalarning o'qitish jarayonidagi ahamiyati tahlil qilinadi. Shuningdek, o‘lchamlarni qisqartirishning samarali usuli bo‘lgan Principal Component Analysis (PCA) amaliyoti yoritiladi. PCA metodikasi, katta hajmdagi ma'lumotlar to‘plamlarida asosiy komponentlarni aniqlashga yordam beradi va bu, ma’lumotlarning yuqori o‘lchamli joylarda qiyinchiliklar keltirib chiqaradigan strukturasini soddalashtiradi. Maqolada PCA metodining ishlash prinsipi, afzalliklari va mashinalarning o‘qitishdagi o‘rni haqida batafsil tahlil beriladi. Shu bilan birga, PCA metodini amaliyotda qo‘llashning asosiy nuqtalari va uning ma’lumotlarni tahlil qilishdagi o‘rinlari ko‘rsatiladi.

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O’ktamov , M. . (2025). MA’LUMOTLARNI NORMALIZATSIYA VA STANDARLASHTIRISH USULLARI. O‘LCHAMLARNI QISQARTIRISH VA PCA (PRINCIPAL COMPONENT ANALYSIS) AMALIYOTI. Наука и инновация, 3(57), 28–32. Retrieved from https://in-academy.uz/index.php/si/article/view/69066

##submission.citations##:

Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Anderson, C. (2014). Machine Learning: A Probabilistic Perspective. MIT Press.

Jolliffe, I. T. (2002). Principal Component Analysis. Springer Series in Statistics.

Tan, P.-N., Steinbach, M., & Kumar, V. (2016). Introduction to Data Mining. Pearson.

Xie, L., & Wang, L. (2015). Application of PCA in Bioinformatics. Bioinformatics Journal.

Shlens, J. (2014). A Tutorial on Principal Component Analysis. arXiv preprint arXiv:1404.1100.

Jolliffe, I. T., & Cadima, J. (2016). Principal Component Analysis: A Review and Recent Developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.

Zhang, L., & Zhou, Z. (2017). PCA-Based Dimensionality Reduction in Image Recognition. International Journal of Computer Science and Information Security, 15(12), 9–18.

Tan, P.-N., & Kumar, V. (2017). Introduction to Data Mining: Pearson New International Edition. Pearson Education Limited.